Visualization of World Population from 1970 to 2022

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Understanding the Data:

#Loading packages
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from IPython.display import HTML

#Loading dataset
df = pd.read_csv("C:/Users/Nathan/Documents/Portfolio/Portfolio Data/World Population EDA/world_population.csv")

#Determining the number of rows and columns in the data
df.shape

(234, 17)

df.head(5)

	Rank	CCA3	Country	Capital	Continent	2022 Population	2020 Population	2015 Population	2010 Population	2000 Population	1990 Population	1980 Population	1970 Population	Area (km²)	Density (per km²)	Growth Rate	World Population Percentage
0	36	AFG	Afghanistan	Kabul	Asia	41128771	38972230	33753499	28189672	19542982	10694796	12486631	10752971	652230	63.0587	1.0257	0.52
1	138	ALB	Albania	Tirana	Europe	2842321	2866849	2882481	2913399	3182021	3295066	2941651	2324731	28748	98.8702	0.9957	0.04
2	34	DZA	Algeria	Algiers	Africa	44903225	43451666	39543154	35856344	30774621	25518074	18739378	13795915	2381741	18.8531	1.0164	0.56
3	213	ASM	American Samoa	Pago Pago	Oceania	44273	46189	51368	54849	58230	47818	32886	27075	199	222.4774	0.9831	0.00
4	203	AND	Andorra	Andorra la Vella	Europe	79824	77700	71746	71519	66097	53569	35611	19860	468	170.5641	1.0100	0.00

The dataset contains population information on 234 different countries and territories within 17 variables:

• Rank : Ranking from 1 to 234 for each country/territory based on current population size
• CCA3 : The three digit country/territory code associated with each country/territory
• Country : The name of the country/territory
• Capital : The name of the capital for the given country/territory
• Continent : The name of the continent for the given country/territory (Asia, Africa, Europe, North America, South America, and Oceania)
• 2022 Population : The 2022 population of the given country/territory
• 2020 Population : The 2020 population of the given country/territory
• 2015 Population : The 2015 population of the given country/territory
• 2010 Population : The 2010 population of the given country/territory
• 2000 Population : The 2000 population of the given country/territory
• 1990 Population : The 1990 population of the given country/territory
• 1980 Population : The 1980 population of the given country/territory
• 1970 Population : The 1970 population of the given country/territory
• Area (km²) : The land mass of the given country/territory in km²
• Denisty (per km²) : The population density of the given country/territory in people per km²
• Growth Rate : The current population growth rate of the given country/territory
• World Population Percentage - The percentage of the total world population residing in the given country/territory

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We can start by ensuring that every column is complete and contains no missing values which can be done by counting the number of instances that occur in each variable column. A quick and easy way to do this is by plotting this result in a bar chart.

#Check each column in the data for missing values
missing = df.notnull().sum(axis=0)

#Generate Bar plot for each variable in the dataset counting the number of non-missing instances
fig = px.bar(x=missing.index,
             y=missing.values,
             text=missing.values, #include the number of non-missing values inside each bar on the plot for clarity
             title='World Population Dataset: Total Number of Data Points (out of 234 rows)'
            )
fig.update_traces(hovertemplate='<br> Variable: %{x} </br> Number of Instances: %{y}')
fig.update_layout(xaxis_title='Dataset Variables',yaxis_title='Number of Instances')
fig.show()

Since every column contains 234 instances, we can assume there are no missing values in the dataset

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To better understand the dataset, we may wish to know how many countries/territories are being accounted for in each continent

#Count the number of countries/territories in each continent
country_counts = df.groupby('Continent')['Country'].count()

#Generate 
fig = px.bar(x=country_counts.index,
             y=country_counts.values,
             color=country_counts.index, #add color to easily differentiate between each continent
             text=country_counts.values, #include the number of countries/territories in each continent on bar plot
             color_discrete_sequence=px.colors.sequential.Peach[::-1],
             title='World Population Dataset: Countries per Continent')
fig.update_traces(hovertemplate='<br> Continent: %{x} </br> Number of Countries Included: %{y}') #edit hovertext for clarity
fig.update_layout(xaxis_title='Continents',yaxis_title='Number of Countries')
fig.show()

• There is clearly an unequal number of countries/territories accounted for in each continent
• Africa has the greatest number of countries/territories accounted for in the data
• South America has the least amount of countries/territories accounted for in the data

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Visualizing the Data:

The data contains two variables which have a clear link to one another as well as population size: Area (km²) and Density (per km²)

fig = px.choropleth(data_frame=df,
                   locations='Country',
                   locationmode='country names',
                   color='Area (km²)',
                   color_continuous_scale=px.colors.sequential.Bluyl,
                   title='Total Land Mass by Country',
                   template='ggplot2',
                   projection='natural earth'
                   )
fig.update_geos(lataxis_showgrid=True,
                lonaxis_showgrid=True,
                showocean=True, oceancolor="LightBlue",
                showlakes=True, lakecolor="LightBlue")
fig.show()

Notice:

• Representing the information in a Winkel Tripel projection makes the most sense since it helps preserve true land mass with minimal distortion
• Russia clearly has the greatest amount of available land mass
• The United State, Canada, Brazil, Austalia, and China seem to be the next largest countries

In a direct comparison, it helps to look at the population density in a similar visual to see if any obvious relations exist

fig = px.choropleth(data_frame=df,
                   locations='Country',
                   locationmode='country names',
                   color='Density (per km²)',
                   color_continuous_scale=px.colors.sequential.OrRd,
                   title='Total Population Density by Country',
                   template='ggplot2',
                   range_color=(0,150),
                   projection='natural earth'
                   )
fig.update_geos(lataxis_showgrid=True,
                lonaxis_showgrid=True,
                showocean=True, oceancolor="LightBlue",
                showlakes=True, lakecolor="LightBlue")
example = fig
fig.show()

Notice:

• Although China had one of the greatest available land masses, it also has one of the greatest population densities
• There seems to be the greatest variation of population denisty on the African continent
• Europe and Asia exhibit the greatest population densities

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Lets begin looking at the current world population numbers and proportions by first inspecting each continents contribution:

#Find the average population by continent and sort values in a descending order
continent_populations = df.groupby('Continent')['2022 Population'].mean().round().sort_values(ascending=False)

#Generate a bar plot to visual each continent's average population
fig=px.bar(x=continent_populations.index,
           y=continent_populations.values,
           color=continent_populations, #color each continents bar according to its population
           text=continent_populations.values, #include average population amount on each bar for clarity
           color_continuous_scale=px.colors.sequential.Darkmint,
           title='Average 2022 Population by Continent'
          )

fig.update_layout(xaxis_title='Continents',
                  yaxis_title='Average Population Count')

fig.update_traces(hovertemplate='<br> Continent: %{x} </br> Average Population: %{y}') #update hover information for inspection

fig.show()

Notice:

• Asia has the greatest average population density compared to all of the other continents
• Asia's average population is nearly the same as all the remaining continents average populations combined
• Although Europe's overall land mass is much smaller than North America's, they have a nearly identical average population

#Generate a pie chart to visualize the proportion each continent contributes to the current total world population
fig = px.pie(df,
             values='2022 Population',
             names='Continent',
             color_discrete_sequence=px.colors.sequential.Magenta[::-1],
             title='Total 2022 Population by Continent',
             hole=0.25
            )

fig.update_traces(textinfo='label+percent+value') #update each slices information for better readability

fig.show()

Notice:

• Asia contributes nearly 60% of the total world population at roughly 4.7 billion people
• Although South America had the second highest average population, it only contributes about 6% of the world population which is the second lowest
• Although Europe has the second lowest average population, it contributes about 10% of the world population which is the third largest

To get a better idea of how the average and total populations for each continent got to the current levels, we can inspect the changes over the past 50 years in similar plots:

#Generate a list to cycle through the past 50 years populations by decades
populations = ['2020 Population','2010 Population','2000 Population','1990 Population','1980 Population','1970 Population']
#Reverse the list to go chronologically
populations.reverse()

#Using a for loop, plot a bar and pie chart for the average population and total population for each continents over the decades
for pop in populations:
    fig = make_subplots(rows=1,cols=2,specs=[[{'type':'xy'},{'type':'domain'}]], #Generate a subplot matrix for each decade
                   subplot_titles = ['Average '+pop,'Total '+pop],
                   horizontal_spacing=0.2,column_widths=[0.7,0.3], y_title='Per Country/Territory')

    fig.add_trace( #Generate a bar plot of the average population for each country by continent
    go.Bar(x=continent_populations.index,
           y=continent_populations.values,
           showlegend=False,
           name='',
           marker=dict(color=px.colors.sequential.Darkmint[::-1]),
           texttemplate='%{y}'
    ),
    row=1,
    col=1
    )

    fig.add_trace( #Generate a pie chart of the total population within each continent including total percentage and amount
    go.Pie(values = df.groupby('Continent')[pop].sum().sort_values(ascending=False),
           labels = df.groupby('Continent')[pop].sum().sort_values(ascending=False).index,
           showlegend=False,
           name='',
           marker=dict(colors=px.colors.sequential.Magenta[::-1]),
           texttemplate='<br>%{label}</br>%{percent}</br>%{value}',
           textposition='outside',
           hole=0.25
    ),
    row=1,
    col=2
    )
    fig.update_layout(title_text=pop+' Demographics by Continent')
    fig.show()

Notice:

• Asia has always had the largest average and total population over the past 50 years
• Europe was the second largest population until the 2000's when Africa surpassed it
• North America, South America, and Oceania have remained the lowest total populations respectively since at least the 1970's

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After looking at each continent as a whole, we can identify which countries and territories have contributed the most and least to the current world population totals:

#Calculate the top 5 most populated countries/territories in 2022
mostpop_country = df.groupby('Country')['2022 Population'].sum().sort_values(ascending=False).head(5)

#Generate a bar plot of the top 5 most populated countries of 2022
fig = px.bar(x=mostpop_country.index,
             y=mostpop_country.values,
             color_discrete_sequence = ['MidnightBlue'],
             text=mostpop_country.values,
             title='Top 5 Most Populated Countries According to 2022 Population'
            )
#Update axis titles for clarity
fig.update_layout(xaxis_title='Country', yaxis_title='Population')
#Update hover information for addition clarity
fig.update_traces(hovertemplate='<br> Country: %{x} </br> 2022 Population: %{y}')

fig.show()

Notice:

• China and India are nearly tied for the largest country populations both hosting populations of roughly 1.4 billion
• China and India hold nearly 50% of the total world population within their borders alone
• The United States is the third most populated country in the world at around 340 million people

#Calculate the top 5 least populated countries/territories of 2022
leastpop_country = df.groupby('Country')['2022 Population'].sum().sort_values(ascending=True).head(5)
leastpop_country = leastpop_country.sort_values(ascending=False)

#Generate a bar plot of the top 5 least populated countries/territories of 2022
fig = px.bar(x=leastpop_country.index,
             y=leastpop_country.values,
             color_discrete_sequence = ['IndianRed'],
             text=leastpop_country.values,
             title= 'Top 5 Least Populated Countires According to 2022 Population'
             )
#Update axis titles for clarity
fig.update_layout(xaxis_title='Country', yaxis_title='Population')
#Update hover information for additional clarity
fig.update_traces(hovertemplate='<br> Country: %{x} </br> 2022 Population: %{y}')
fig.show()

Notice:

• The Vatican City is the least populated territory in the world only hosting about 500 residents
• The 5 least populated countries in the world all have less than 4,500 people residing within eacha of them

We can try to determine if there are any changes in these rankings by looking at the most and least populated countries and territories of the past 50 years:

#Generate list of decades to cycle through for plotting
populations = ['2020 Population','2010 Population','2000 Population','1990 Population','1980 Population','1970 Population']
#Set list to chronological order
populations.reverse()

#Using a for loop, plot the top 5 least and most populated countries/territories for each decade over the past 50 years
for pop in populations:
    #Calculate the least and most populated countries/territories of each decade
    mostpop_country = df.groupby('Country')[pop].sum().sort_values(ascending=False).head(5)
    leastpop_country = df.groupby('Country')[pop].sum().sort_values(ascending=True).head(5)
    leastpop_country = leastpop_country.sort_values(ascending=False)

    #Generate subplot matrix for each decade
    fig = make_subplots(rows=1,cols=2,specs=[[{'type':'xy'},{'type':'xy'}]],
                   subplot_titles=['Top 5 Most Populated Countries','Top 5 Least Populated Countries'],
                   y_title='Population', x_title='Countries')
    #Generate bar chart for top 5 most populated countries of the decade
    fig.add_trace(
    go.Bar(x=mostpop_country.index,
           y=mostpop_country.values,
           texttemplate='%{y}',
           name='',
           showlegend=False,
           marker=dict(color='MidnightBlue')
    ),
    row=1,
    col=1
    )
    #Generate bar chart for top 5 least populate countries of the decade
    fig.add_trace(
    go.Bar(x=leastpop_country.index,
           y=leastpop_country.values,
           texttemplate='%{y}',
           name='',
           showlegend=False,
           marker=dict(color='IndianRed')
    ),
    row=1,
    col=2
    )
    #Include a main title indicating which decade the subplots are refering to
    fig.update_layout(title_text=pop)
    fig.show()

Notice:

• The top 4 most populated countries have remained the same over the past 50 years: China, India, United States, and Indonesia
• The Vatican City has remained the least populated territory in the world for the past 50 years and has actually shrunk in population
• Saint Barthelemy's population doubles from 1970 to 1990 until it eventually made its way out of the 5 least populatedc countires and territories

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Since the data contains information on the world population broken down by each country and territory's contribution, it may be easier to visualize each countries impact by viewing the information on a choropleth map. This makes it possible to view the aggregated population data in a geographic way.

Lets start by looking at a choropleth map of the current 2022 world population

fig = px.choropleth(data_frame=df,
                   locations='Country',
                   locationmode='country names',
                   color='2022 Population',
                   color_continuous_scale=px.colors.sequential.RdBu[::-1],
                   range_color=(0,1000000000),
                   title='World Map Indicating 2022 Populations',
                   template='ggplot2',
                   projection='natural earth')
fig.update_geos(lataxis_showgrid=True,
                lonaxis_showgrid=True,
                showocean=True, oceancolor="LightBlue",
                showlakes=True, lakecolor="LightBlue")

Notice:

• X
• XX
• XXX

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We can also view a choropleth map of the world population over the past 50 years and see how the population numbers change over the decades through an animation.

#List of the past 5 decades to inspect
years = ['2020 Population','2010 Population', '2000 Population', '1990 Population','1980 Population', '1970 Population']

#Reverse order to see oldest to most recent
years.reverse()

#Copy data
country_pop_year = df

#Drop irrelevant columns
country_pop_year = country_pop_year.drop(['Area (km²)', 'Density (per km²)', 'Growth Rate','World Population Percentage'],
                                         axis=1)
#Reconfigure data to have all different populations year data in one column with another column 'year' as identifier
country_pop_year = pd.melt(country_pop_year,id_vars=['Country'],value_vars=years)

#Rename new data set columns to match contents
country_pop_year = country_pop_year.rename(columns={'variable':'Year','value':'Population'})

#Remove ' Population' from indentifier column so it only include year (1970, 1980, etc.)
country_pop_year['Year'] = country_pop_year['Year'].str.replace(' Population','')

#Generate a choropleth animation showing the change in country/territory populations for each decade from 1970-2020
fig = px.choropleth(data_frame=country_pop_year,
                   locations='Country', #Column used to identify countries
                   locationmode='country names', 
                   color='Population', #Column used to identify color intensity
                   animation_frame='Year', #Column used to idenify each animation frame
                   color_continuous_scale=px.colors.sequential.RdBu[::-1],
                   range_color=(0,1000000000), #set limit on colorscale, India and China are so large the rest seem unchanged
                   title='World Map Indicating Populations: 1970 - 2020',
                   template='ggplot2',
                   projection='natural earth') #change map projection type to Winkel Tripel

#Include longitude, latitude, and water markings for clarify and visual appeal
fig.update_geos(lataxis_showgrid=True,
                lonaxis_showgrid=True,
                showocean=True, oceancolor="LightBlue",
                showlakes=True, lakecolor="LightBlue")

Notice:

• This map clearly shows the high world population density centered in China and India
• Although some African nation's populations seem to be growing, others seem to remain nearly unchanged in a 50 year span
• European countries appear to have the smallest changes in population numbers which is also visable in earlier pie charts

Since nearly half of the current world population reside in China and India, it makes sense to isolate Asia and take a closer look at their changes over the past 50 years.

#Generate the same choropleth map as before, this time changing the scope of the map from the entire world to only Asia
fig = px.choropleth(data_frame=country_pop_year,
                   locations='Country',
                   locationmode='country names',
                   color='Population',
                   animation_frame='Year',
                   color_continuous_scale=px.colors.sequential.RdBu[::-1],
                   range_color=(0,1000000000),
                   title='Asia Population Density Map: 1970 - 2020',
                   template='ggplot2',
                   scope = 'asia')
fig.update_geos(lataxis_showgrid=True,
                lonaxis_showgrid=True,
                showocean=True, oceancolor="LightBlue",
                showlakes=True, lakecolor="LightBlue")
fig.show()

Notice:

• In the past 5 decades, India's total population nearly triples in magnitude showing a very high growth rate in comparison to the rest of the world
• China shows a similar increase nearly doubling it's total population as well in the same amount of time
• Of the top 5 most populated countries in the world, four of them are located in Asia: China, India, Pakistan, and Indonesia
• Indonesia and Pakistan also hold large population especially when taking their total land mass into account. For reference, these two countries follow as the fourth and fifth most populated countries in the world behind the United States at third. However, their land masses are between 4 and 10 times smaller than the United States making their population densities one of the highest in the world.

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